![International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 3 Issue 6, October 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD29266 | Volume – 3 | Issue – 6 | September - October 2019 Page 934
Study of the Effects of Process Parameters on Tool Wear Rate
in Electrical Discharge Machining by Taguchi Method
Minh - Nguyen Duc, Tai – Bui Tien
Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam
ABSTRACT
EDM machines are used to cut conductive metals of any hardness or that are
difficult or impossible to cut with traditionalmethods.Theproblemof arriving
at the optimum levels of the operating parameters has attracted the attention
of the researchers and practicing engineers for a very long time. This paper
introduces the research results of toolwearrateofSKD11 steelafterEDM.The
influence of the process paramaters to the surface roughness (Ra) issurveyed
and the optimal value Ra (Ratoiuu) also be established. The process
parameters: current (I), pusle on time (ton), pulse off time (tof) and voltage is
used in research on SR in EDM. Taguchi method is used in this study.
KEYWORDS: TWR; EDM; PMEDM; Taguchi method
How to cite this paper: Minh - Nguyen
Duc | Tai – Bui Tien "Study of the Effects
of Process Parameters on ToolWearRate
in Electrical Discharge Machining by
Taguchi Method"
Published in
International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-3 |
Issue-6, October 2019, pp.934-936, URL:
https://www.ijtsrd.com/papers/ijtsrd29
266.pdf
Copyright © 2019 by author(s) and
International Journal of Trend in
Scientific Research and Development
Journal. This is an Open Access article
distributed under
the terms of the
Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/b
y/4.0)
1. INTRODUCTION
EDM is a non traditional machining process that hasbecome
a well established machining option in manufacturing
industries throughout the world and has replaced drilling,
milling, grinding and other traditional machiningoperations.
The machines also specialize in cutting complex contours or
fragile geometries that would be difficult to be produced
using conventional cutting methods. Machine tool industry
has made exponential growth in its manufacturing
capabilities in last decade but still machine tools are not
utilized at their full potential. This limitationis aresultofthe
failure to run the machine tools at their optimum operating
conditions. With the continuous process improvement in
EDM, the demand for high machining precision with low
surface roughness at relatively high machining rates arise in
die, mold and tool manufacturing industries.
Optimization of process parameters ofEDM has been treated
as single-objective optimization process. Taguchi’s method
has been employed by many researchers as single-objective
optimization technique to find the optimal combination of
process parameters by considering each performance
measure as a separate objective [1]. [2] employed Taguchi’s
method to find the best combination of parameters for
surface finish characteristics. [3] optimized the process
parameters of EDM by Taguchi’s method. [4] used Taguchi
dynamic approach coupled with proposed ideal function
model to optimize a high-speed EDM process. [5] obtained
optimum parameter settings during EDM by Taguchi’s
approach. [6] optimized the machining parameters
in EDM for SKH 57 steel. There are many authors to citewho
have employed Taguchi’s method as optimization technique
in EDM [7].
The intent of the present study is to study the effect of
different input parameters, namely, workpiecematerial, tool
material, polarity, pulse-on time, current, pulse-off time,and
powder concentration and some their interactions on the
TWR in PMEDM using Ti powder. The effect of various input
parameters on output response have been analyzed using
Analysis of Variance (ANOVA). Main effect plot and
interaction plot for significant factors have been used to
determine the optimal design for TWR.
2. Experimental procedure
Through conducting the EDM, experiments involve I, U, ton,
tof. SKD11 steel havingdimensions of 15x15x20 mm. Copper
(Cu) electrode was used in the EDM process. The dielectric
fluid of the experiment is D323 oil. The CNC- CM323C die-
Sinking machining (CHMER, Taiwan) has been used to
experiment. Their measurements areperformed on eachtest
sample, and the result is the average value of each
measurement. The surface roughness isalsomeasured using
surf-test (Model: SV–2100, Mitutoyo Japan).
The Taguchi method is used to design experiments based on
the orthogonal matrix, and is used to assess the process
IJTSRD29266](https://image.slidesharecdn.com/154studyoftheeffectsofprocessparametersontoolwearrateinelectricaldischargemachiningbytaguchi-191123104428/85/Study-of-the-Effects-of-Process-Parameters-on-Tool-Wear-Rate-in-Electrical-Discharge-Machining-by-Taguchi-Method-1-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD29266 | Volume – 3 | Issue – 6 | September - October 2019 Page 936
Figure2. Main effects plot for SR.
4. Conclusion
The effect of parameters i.e. pulse on time, pulse off time,
current and voltage were evaluated using ANOVA and
factorial design analysis. The purpose of the ANOVA was to
identify the important parameters in prediction SR. Some
results consolidated from ANOVA and plots aregiven below:
pulse on time, current, pulse on time were the significant
factors which affects the SR. Voltage was found to be
insignificant for SR. Only SKD11 had been used. Other
materials such as SKD61, OHNS die steel and tungsten hot
work die steel can be machined.
Reference
[1] Bharti, Pushpendra S., Maheshwari, S., & Sharma, C.
(2010). A comparative study of Artificial Neural
Network training algorithms for modeling of Electric
Discharge Machining process. Journal of Mechanical
Engineering (Slovak), Vol. 61, No. 5-6, pp. 323-340.
[2] Dev, A., Patel, K.M., Pandey, S. and Aravindan,S.(2009).
Machining charactersistics andoptimization of process
parameters in micro-EDM of SiCp-Al composites.
International Journal of Manufacturing Research, 4(4)
pp 458-480.
[3] Ghani, J. A., Choudhary, I. A., & Hassan, H. H. (2004).
Application of Taguchi method in the optimization of
end milling parameters. Journal of Material Processing
Technology, 145, pp. 84-92.
[4] Ho, K.H., & Newman, S.T. (2003). State of the art
electrical discharge machining (EDM). International
Journal of Tools Manufacturing, 43, pp. 1287-1300.
[5] Jain, N. K., Jain, V. K., Deb, K. (2007). Optmization of
process parameters of mechanical type advanced
machining processes using genetic algorithms.
International Journal of Machine Tools and
Manufacture, pp. 900-919.
[6] Jung, J. H., & Kwon, W.T. (2010). Optimization of EDM
process for multiple performance characteristics using
Taguchi method and Greyrelationalanalysis,Journalof
Mechanical Science and Technology, 24 (5), pp. 1083-
1090.
[7] Marafona, J. (2007). Black layer characterisation and
electrode wear ratio in electrical discharge machining
(EDM). Journal of MaterialProcessingTechnology,184,
pp. 27-31.
[8] R. Roy, A Primer on the Taguchi Method, New York:Van
Nostrand Reinhold, 1990.](https://image.slidesharecdn.com/154studyoftheeffectsofprocessparametersontoolwearrateinelectricaldischargemachiningbytaguchi-191123104428/85/Study-of-the-Effects-of-Process-Parameters-on-Tool-Wear-Rate-in-Electrical-Discharge-Machining-by-Taguchi-Method-3-320.jpg)
Study of the Effects of Process Parameters on Tool Wear Rate in Electrical Discharge Machining by Taguchi Method
- 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 3 Issue 6, October 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD29266 | Volume – 3 | Issue – 6 | September - October 2019 Page 934 Study of the Effects of Process Parameters on Tool Wear Rate in Electrical Discharge Machining by Taguchi Method Minh - Nguyen Duc, Tai – Bui Tien Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam ABSTRACT EDM machines are used to cut conductive metals of any hardness or that are difficult or impossible to cut with traditionalmethods.Theproblemof arriving at the optimum levels of the operating parameters has attracted the attention of the researchers and practicing engineers for a very long time. This paper introduces the research results of toolwearrateofSKD11 steelafterEDM.The influence of the process paramaters to the surface roughness (Ra) issurveyed and the optimal value Ra (Ratoiuu) also be established. The process parameters: current (I), pusle on time (ton), pulse off time (tof) and voltage is used in research on SR in EDM. Taguchi method is used in this study. KEYWORDS: TWR; EDM; PMEDM; Taguchi method How to cite this paper: Minh - Nguyen Duc | Tai – Bui Tien "Study of the Effects of Process Parameters on ToolWearRate in Electrical Discharge Machining by Taguchi Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456- 6470, Volume-3 | Issue-6, October 2019, pp.934-936, URL: https://www.ijtsrd.com/papers/ijtsrd29 266.pdf Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/b y/4.0) 1. INTRODUCTION EDM is a non traditional machining process that hasbecome a well established machining option in manufacturing industries throughout the world and has replaced drilling, milling, grinding and other traditional machiningoperations. The machines also specialize in cutting complex contours or fragile geometries that would be difficult to be produced using conventional cutting methods. Machine tool industry has made exponential growth in its manufacturing capabilities in last decade but still machine tools are not utilized at their full potential. This limitationis aresultofthe failure to run the machine tools at their optimum operating conditions. With the continuous process improvement in EDM, the demand for high machining precision with low surface roughness at relatively high machining rates arise in die, mold and tool manufacturing industries. Optimization of process parameters ofEDM has been treated as single-objective optimization process. Taguchi’s method has been employed by many researchers as single-objective optimization technique to find the optimal combination of process parameters by considering each performance measure as a separate objective [1]. [2] employed Taguchi’s method to find the best combination of parameters for surface finish characteristics. [3] optimized the process parameters of EDM by Taguchi’s method. [4] used Taguchi dynamic approach coupled with proposed ideal function model to optimize a high-speed EDM process. [5] obtained optimum parameter settings during EDM by Taguchi’s approach. [6] optimized the machining parameters in EDM for SKH 57 steel. There are many authors to citewho have employed Taguchi’s method as optimization technique in EDM [7]. The intent of the present study is to study the effect of different input parameters, namely, workpiecematerial, tool material, polarity, pulse-on time, current, pulse-off time,and powder concentration and some their interactions on the TWR in PMEDM using Ti powder. The effect of various input parameters on output response have been analyzed using Analysis of Variance (ANOVA). Main effect plot and interaction plot for significant factors have been used to determine the optimal design for TWR. 2. Experimental procedure Through conducting the EDM, experiments involve I, U, ton, tof. SKD11 steel havingdimensions of 15x15x20 mm. Copper (Cu) electrode was used in the EDM process. The dielectric fluid of the experiment is D323 oil. The CNC- CM323C die- Sinking machining (CHMER, Taiwan) has been used to experiment. Their measurements areperformed on eachtest sample, and the result is the average value of each measurement. The surface roughness isalsomeasured using surf-test (Model: SV–2100, Mitutoyo Japan). The Taguchi method is used to design experiments based on the orthogonal matrix, and is used to assess the process IJTSRD29266
- 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD29266 | Volume – 3 | Issue – 6 | September - October 2019 Page 935 parameters. This method included a simple experimental design and standardization. The experimental design of Taguchi method was implemented by the orthogonalmatrix (table 1) for placement of the process parameters, which were examined by their levels with the smallest number of experiments during the time as well as the least expensive. The selection of tables was based on the number of parameters and their change rates. ANOVA was based on data obtained from Taguchi’s experimental design and was used to select new parameter values to optimize the quality characteristics. Data from the table was analyzed by using charts, pictures, ANOVA and the Fisher ratio test (F). Based on the 20 degrees of freedom, theL25orthogonal arraysuited the present requirements as it had 20 degreesof freedom, as shown in Table 2. Table1. The L25 array with experimental conditions and measured responses. Exp. No I U Ton Tof TWR (mg/min) 1 1 30 18 9 0.2500 2 1 40 25 12 0.1500 3 1 50 37 18 0.1220 4 1 60 50 25 0.1110 5 1 70 75 37 0.0330 6 2 30 25 18 0.1960 7 2 40 37 25 0.2000 8 2 50 50 37 0.0830 9 2 60 75 9 0.0740 10 2 70 18 12 0.2160 11 3 30 37 37 0.1750 12 3 40 50 9 0.1500 13 3 50 75 12 0.1500 14 3 60 18 18 0.3750 15 3 70 25 25 0.3000 16 4 30 50 12 0.1000 17 4 40 75 18 0.1000 18 4 50 18 25 1.2600 19 4 60 25 37 0.9160 20 4 70 37 9 0.2500 21 5 30 75 25 0.1000 22 5 40 18 37 1.7000 23 5 50 25 9 0.6000 24 5 60 37 12 0.2000 25 5 70 50 18 0.1000 3. Results and discussion Theresults for SRwere analyzed usingANOVA foridentifyingthe significant factors affectingtheperformancemeasures.ANOVA for the mean SR at 99 % confidence interval is given in Table 2. The variance data for each factor and their interactions were F- tested to find the significance of each. ANOVA table shows that I (F value 103.35), ton (F value 16.79,41), tof (F value 9.54) significantaffect the SR. U is insignificant to affect SR. It is observed that the I is the most significant factor which contributes SR. Main effects plot for TWR are shown in the Figure 1 that shows SR increases with increase in current from 1 Amp to 5 Amp and also increases with increase in tof from 12µs to37 µs. Table3shows ranks tovarious factors;I has highest rank,most significant that affecting SR. The U is least significant in SR. Table2. ANOVA of SR Source DF SS V F P I (A) 4 278.362 69.5904 103.34 0.000 U (B) 4 3.314 0.8285 1.23 0.371 ton (C) 4 45.217 11.3043 16.79 0.001 tof (D) 4 25.689 6.4221 9.54 0.004 Error 8 5.387 0.6734 Total 24 357.969 Table3. Response table for a mean of SR. Level I U Ton Toff 1 3.8877 -1.6153 -4.2515 -1.2768 2 -1.6028 -1.8066 -2.8072 -0.6688 3 -2.5343 -2.3956 -2.1086 -2.2887 4 -4.5948 -2.6028 -0.6385 -2.9541 5 -5.7278 -2.1517 -0.7661 -3.3836 Delta 9.6155 0.9875 3.6130 2.7148 Rank 1 4 2 3
- 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD29266 | Volume – 3 | Issue – 6 | September - October 2019 Page 936 Figure2. Main effects plot for SR. 4. Conclusion The effect of parameters i.e. pulse on time, pulse off time, current and voltage were evaluated using ANOVA and factorial design analysis. The purpose of the ANOVA was to identify the important parameters in prediction SR. Some results consolidated from ANOVA and plots aregiven below: pulse on time, current, pulse on time were the significant factors which affects the SR. Voltage was found to be insignificant for SR. Only SKD11 had been used. Other materials such as SKD61, OHNS die steel and tungsten hot work die steel can be machined. Reference [1] Bharti, Pushpendra S., Maheshwari, S., & Sharma, C. (2010). A comparative study of Artificial Neural Network training algorithms for modeling of Electric Discharge Machining process. Journal of Mechanical Engineering (Slovak), Vol. 61, No. 5-6, pp. 323-340. [2] Dev, A., Patel, K.M., Pandey, S. and Aravindan,S.(2009). Machining charactersistics andoptimization of process parameters in micro-EDM of SiCp-Al composites. International Journal of Manufacturing Research, 4(4) pp 458-480. [3] Ghani, J. A., Choudhary, I. A., & Hassan, H. H. (2004). Application of Taguchi method in the optimization of end milling parameters. Journal of Material Processing Technology, 145, pp. 84-92. [4] Ho, K.H., & Newman, S.T. (2003). State of the art electrical discharge machining (EDM). International Journal of Tools Manufacturing, 43, pp. 1287-1300. [5] Jain, N. K., Jain, V. K., Deb, K. (2007). Optmization of process parameters of mechanical type advanced machining processes using genetic algorithms. International Journal of Machine Tools and Manufacture, pp. 900-919. [6] Jung, J. H., & Kwon, W.T. (2010). Optimization of EDM process for multiple performance characteristics using Taguchi method and Greyrelationalanalysis,Journalof Mechanical Science and Technology, 24 (5), pp. 1083- 1090. [7] Marafona, J. (2007). Black layer characterisation and electrode wear ratio in electrical discharge machining (EDM). Journal of MaterialProcessingTechnology,184, pp. 27-31. [8] R. Roy, A Primer on the Taguchi Method, New York:Van Nostrand Reinhold, 1990.